There has been a growing use of regression discontinuity design rdd, introduced by thistlewaite and campbell 1960, in evaluating impacts of development programs. A standard way of graphing the data is to divide the assignment variabled here into a number of bins, making sure there are two separate bins on each side of the cutoff point to avoid having treated and untreated observations mixed together in the same bin. Evidence from a regression discontinuity approach by raffaello bronzini and eleonora iachini. Cattaneoy luke keelez roc o titiunikx gonzalo vazquezbare february 4, 2015 abstract we consider a regression discontinuity design where the treatment is received if a.
Using spss to analyze data from a regressiondiscontinuity design. One sufficient, though not necessary, condition is if the treatment assignment is as good as random at the threshold for. Identi cation in regression discontinuity designs with. Is a discontinuity regression the correct method to prove this and if so, how does it work, please. This methodological brief introduces the reader to the regression discontinuity design rdd, which is a method that when used correctly can yield estimates of research treatment effects that are equivalent to those obtained through randomized control trials and can therefore be used to infer causality. Yeaton 2006 used the regression discontinuity design to explore the effectiveness. During the last decade, an increasing number of political scientists have turned to regressiondiscontinuity rd designs to estimate causal effects. It is usually desirable to have variability in the program group as well. Can be more efficient than other software spss, etc can handle more complex analyses. Statistical power for regression discontinuity designs in. This methodological brief introduces the reader to the regression discontinuity design rdd, which is a method that when used correctly can yield estimates of research treatment effects that are equivalent to those obtained through randomized control. Im writing a political economy masters thesis regarding the effects of the euro crisis on the rise of separatism in spain catalan independence for example.
Using a regression discontinuity design to estimate the. Ibm fuzzy regression discontinuity designs twostage. Jul 07, 2017 this video provides an introduction to regression discontinuity analysis. Cq press your definitive resource for politics, policy and people. The wwc continues to refine its processes, develop new standards, and create new products. Estimating causal effects using a regressiondiscontinuity approach stata textbook examples. Software for bandwidth and codes for the manipulation. The regressiondiscontinuity rd design is widely used in applied work. Ibm spss regression enables you to predict categorical outcomes and apply a wide range of nonlinear regression procedures. This document contains revised regression discontinuity design rdd standards that have been developed by the wwc in consultation with experts and will be implemented once they become part of a new wwc procedures and standards handbook, planned for 2017. Mar, 2017 i am running a regression discontinuity analysis and i am trying to understand which are the packages and commands most suitable to get consistent result with minimal effort meaning that if one command do all the work by itself it is better the steps of the analysis are the usual ones. Getting the most out of testing in directinternet marketing.
Computationally intensive one regression per data point for each data point, run a weighted linear regression linear or polynomials on x using all the observations within a window. This video is part of a project at the univeristy of amsterdam in which instruction videos were produced to supplement a. The regression discontinuity design rdd can be used to estimate the impact of an intervention in cases in which a randomized controlled trial rct is not feasible. Fuzzy regression discontinuity designs twostage least. I demonstrate how to perform a linear regression analysis in spss. Interrupted time series, and regression discontinuity models. This design can be applied in any context where a particular treatment is administered according to a prespecified rule guideline linked to a continuous. Misunderstandings about the regression discontinuity design in the study of close elections. Regression discontinuity design rdd is a quasiexperimental evaluation option that measures the impact of an intervention, or treatment, by applying a treatment assignment mechanism based on a continuous eligibility index which is a variable with a continuous distribution. Polynomials should not be used in regression discontinuity designs \we argue that estimators for causal e ects based on higher order. Regression discontinuity design in gifted and talented. Regression discontinuity world bank sief aphrc impact evaluation training 2015 owen ozier development research group the world bank 6 may 2015 owen ozier the world bank regression discontinuity 6 may 2015 1 9. This video is part of a project at the univeristy of amsterdam in which instruction.
This chapter provides an overview of regression discontinuity rd designs for social science researchers. Regressiondiscontinuity analysis research methods knowledge. Can be either a statistically significant change in slope or y. The algorithms documents are designed as a resource. Sage video bringing teaching, learning and research to life. The analysis of the regressiondiscontinuity design in r eric. Rddtools is a new r package under development, designed to offer a set of tools to run all the steps required for a regression discontinuity design rdd analysis, from primary data visualisation to discontinuity estimation, sensitivity and placebo testing. The regression discontinuity design is a research design that overcomes some of ethical problems with experimental designsbut also overrides some of the limitations to the internal validity of quasiexperimental designs.
Regression discontinuity and practical applications. Logistic2x2x2 see binary logistic regression with spss. Each outcome was analyzed separately using the spss 22. We discuss similarities and differences between these packages and provide directions on how to use them effectively. Determining power and sample size using sas stat software. The accepted recommendation is to use local linear regression, as opposed to standard nonparametric kernal regression. Using regression discontinuity design for program evaluation. If none of these options is chosen, the program will generate the highest order of. You can use any software such as spss or stata etc. This estimate pertains to average treatment effects for units right at the cutoff.
Sage business cases real world cases at your fingertips. Regression discontinuity world bank sief aphrc impact. Manipulation of the running variable in the regression discontinuity design. Dec 16, 2015 there has been a growing use of regression discontinuity design rdd, introduced by thistlewaite and campbell 1960, in evaluating impacts of development programs. This video provides an introduction to regression discontinuity analysis.
The idea is to show that the crisis has acted as an exogenous shock, causing a sudden rise in separatism. Whether or not students benefit from the program is a discrete function of their score. Part 2 explains how in theory a regression discontinuity analysis can identify an average effect of treatment for a population and how different types of regression discontinuity analyses sharp versus fuzzy can identify average. Regression discontinuity designs in social sciences1. Ibm fuzzy regression discontinuity designs twostage least. Regression discontinuity rd analysis is a rigorous nonexperimental1 approach that can be used to estimate program impacts in situations in which candidates are selected for treatment based on whether their value for a numeric rating exceeds a designated threshold or cutpoint. Aug 05, 2016 randomized controlled trials in a regression discontinuity framework. University of miami miami, fl see all articles by this author. Austin nichols simulation results indicate that the marginal effects may be less sensitive than the latent index function parameters to biprobit assumption violations.
Leveraging the power of regression discontinuity designs for program. Regrdiscont see using spss to analyze data from a regressiondiscontinuity design. Regression discontinuity practical applications basic intuition ii we see these institutional rules all the time cannot get medicaid unless income is below 150 percent of the poverty line must have a gpa of 3. The regression discontinuity design showed to be a valid alternative to a randomized controlled trial for estimating treatment effects. Proceedings of the 2008 joint statistical meeting pp. Improving causal inference in educational and social science research by richard j. Regression discontinuity design rdd in brief this method was developed to estimate treatment e. Although the growth of rd designs has stimulated a wide discussion about rd assumptions and estimation strategies, there is no. Regression discontinuity designs in economics david s. In this video i give you a prototypical situation where rd. Regression discontinuity for causal effect estimation in. Published in volume 6, issue 4, pages 1004 of american economic journal.
It is one of the most credible quasiexperimental research designs for identi. You can find an intro to the command in cattaneo, calonico, and titiuniks stata journal paper robust datadriven inference in the regressiondiscontinuity design. Posttest scores in the treatment group are better predicted by a new regression line than the regression line of the comparison group. Stated differently, an effect occurs if there is a discontinuity in the two regression lines at the cutoff. Willett this is one of the books available for loan from academic technology services see statistics books for loan for other such books, and details about borrowing.
Jan 30, 2015 when the circumstances are right, regression discontinuity can be an excellent way to extract causal estimates from observational data. The analysis of the regressiondiscontinuity design in r. When the assignment variable is a random number that is generated by the researcher, the regression discontinuity design is equivalent to a randomized controlled trial. Feb 11, 20 he was presenting some of his work on nonparametric regression discontinuity design which i found interesting. These designs were first introduced in the evaluation literature by thistlewaite and campbell 1960. Weights give more importances to observations close to data point predicted y, y, is then the \smoothed y i. In regression discontinuity rd designs for evaluating causal effects of interventions, assignment to a treatment is determined at least partly by the value of an observed covariate lying on either side of a fixed threshold. In contrast to the sharp regression discontinuity design, a fuzzy regression discontinuity design frdd does not require a sharp discontinuity in the probability of assignment but is applicable as long as the probability of assignment is different from zero, and in particular greater than zero though less than one. The basic idea of regression discontinuity design rdd is the following. The regressiondiscontinuity design is seen as a useful method for determining whether a program or treatment is effective. All analyses were performed using spss version 22 and r statistical software version 3. The regression discontinuity design rdd is an econometric quasiexperimental design aimed at estimating the causal effects of a treatment by exploiting naturally occurring treatment rules. The basic rd design is a twogroup pretestposttest model as indicated. Unstandardized regression coefficients for covariates.
The variables i was thinking of using would be time v. Robust datadriven inference in the regression discontinuity design. Using regression discontinuity design for program evaluation hyunshik lee1 and tom munk1 1westat, 1650 research blvd. Maximize your purchasing power with flexible payment options and competitive rates for ibm software, services, systems and solutions. Introduction a large proportion of the high school graduates who attend a postsecondary institution take at least one developmental, remedial, or basic skills course in either mathematics or english. Apr 20, 2020 the rdrobust package provides stata and r implementations of statistical inference and graphical procedures for regression discontinuity designs employing local polynomial and partitioning methods. The first part will begin with a brief overview of the spss environment, as well simple data exploration techniques to ensure accurate analysis using simple and multiple regression.
Regression discontinuity design has the potential to be effec tive in nearly any. Reversepattrguilt effect of plaintiff physical attractiveness on jurors certainty that he was sexually harassed by the defendant. Dr gianluca baio and the research group statistics for health economic evaluation, in collaboration with dr marcos vera hernandez and cemmap have organised a oneday workshop on methodological issues and applications in economics, statistics and epidemiology with the regression discontinuity design. In statistics, econometrics, political science, epidemiology, and related disciplines, a regression discontinuity design rdd is a quasiexperimental pretestposttest design that elicits the causal effects of interventions by assigning a cutoff or threshold above or below which an intervention is assigned. Introduction to regression with spss this seminar will introduce some fundamental topics in regression analysis using spss in three parts.
Graphs in regression discontinuity design in stata or r. Identi cation in regression discontinuity designs with multiple cuto s matias d. The regression discontinuity design theory and applications the harvard community has made this article openly available. How can i implement this analysis with the regression module in spss statistics. Properly estimating the treatment effect requires twostage least squares regression. It provides point estimators, confidence intervals estimators, bandwidth selectors, automatic rd plots, and other related features. If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Religion see three way hierarchical log linear analysis. In the popular regression discontinuity rd design, the use of graphical analysis has been strongly advocated because it provides both easy presentation and transparent validation of the design. Regression discontinuity design in stata part 1 stata. Estimating causal effects using a regression discontinuity approach stata textbook examples. Lee and thomas lemieux this paper provides an introduction and user guide to regression discontinuity rd designs for empirical researchers. An intuitive introduction to regression discontinuity youtube. Journal of econometrics, 1422, february 2008, pages 698.
Pps delayed posttest, and gmade posttest serving as outcomes. Regression discontinuity design in stata part 1 stata daily. Ibm spss can be set up to analyze a data set using the. The regressiondiscontinuity design builds on the preexperimental static group comparison design by introducing a series of comparison groups, addressing the issue of selection as a potential rival explanation. Regression discontinuity rd analysis is a rigorous nonexperimental. The regression discontinuity design theory and applications. Assignment to the experimental or comparison group is based on the value of some variable. It is widely used in many fields, such as psychology, sociology, market research, business, and government. Graphical presentation of regression discontinuity results. Adams phd3 1president, linden consulting group, ann arbor, mi, usa. This website contains both the software and codes for supporting the use of.
As ive said im beginning to study statistics and have read a bit on regression discontinuity designs but i dont know if all the computations to get the discontinuity graph and the local average effect should be done by hand or if theres a stata command that may help im thinking of a sharp regression discontinuity design as a. It is useful to distinguish between two general settings, the sharp and the fuzzy regression discontinuity srd and frd from hereon designs e. It presents the conceptual framework behind the research design, explains when rd is likely to be valid or invalid, draws a parallel between rd and randomized experiments, and summarizes dif. Analyzing data from a regression discontinuity study quantitative. The rdrobust package provides stata and r implementations of statistical inference and graphical procedures for regression discontinuity designs employing local polynomial and partitioning methods. Misunderstandings about the regression discontinuity. Regression discontinuity design requires that all potentially relevant variables besides the treatment variable and outcome variable be continuous at the point where the treatment and outcome discontinuities occur. How to rule out confounding variables in cross sectional. Regression discontinuity design dr simon moss sicotests. Using a regression discontinuity design to estimate the impact of placement decisions in developmental math. He was presenting some of his work on nonparametric regression discontinuity design which i found interesting.
The regression discontinuity design showed to be a valid. Using regression discontinuity to estimate the effects of. Counterfactual outcomes at the cutpoint of a regression discontinuity design 59 14 the probability of receiving treatment as a function of the rating 62 15 illustrative regression discontinuity analyses 64. Sage reference the complete guide for your research journey. Article combining the regression discontinuity design and propensity. Regression discontinuity design for crosssectional data.
See cook 2008 for a thorough account of the multidisciplinary history of rdd. This article describes the analysis of regression discontinuity designs rdds using the r packages rdd, rdrobust, and rddtools. Software for regression discontinuity designs show all authors. Regression discontinuity rd is a twogroup, prepost, quasiexperimental method for evaluating the effect of an intervention. Feb 25, 2014 as ive said im beginning to study statistics and have read a bit on regression discontinuity designs but i dont know if all the computations to get the discontinuity graph and the local average effect should be done by hand or if theres a stata command that may help im thinking of a sharp regression discontinuity design as a first step. It presents the basic theory behind the research design, details when rd is likely to be valid or invalid given economic incentives. Sage books the ultimate social sciences digital library. Stata module for regression discontinuity estimation. I would like to implement a fuzzy regression discontinuity design. Discontinuity design relative to an otherwise comparable randomized. How to rule out confounding variables in cross sectional study. Stata module to perform regression discontinuity, statistical software components s458238, boston college department of economics.